Novel methodology to couple decline curve analysis with <scp>CFD</scp> reservoir simulations for complex shale gas reservoirs
Bibliographic record
Abstract
Abstract Shale reservoirs are highly complex and are difficult to study using conventional reservoir simulation tools. This study introduces a novel methodology for estimating production from complex shale gas reservoirs by coupling decline curve analysis (DCA) with computational fluid dynamics (CFD) simulations. The proposed method uses exponential DCA to analyze production data from a dual porosity–permeability shale gas transport model. These complexities include fracture characteristics, geomechanical properties, nanopore confinement effects, and multiple flow mechanisms contributing to the total production performance. The shale gas transport model is validated through historical production data from Marcellus shale. The new methodology also tests fracture characteristics. It shows that increased porosity and permeability will increase the recoverable reserves but will have varying effects on the decline rate. The paper demonstrates the advantages of the proposed methodology over conventional reservoir simulation tools. It provides insights into the factors affecting shale gas production performance through the inclusion of the complexities of an unconventional shale gas reservoir. The paper provides a proof of concept on the particular reservoir of which the field data is provided—Barnett and Marcellus Shale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".